Add tanh method to tensor

pygrad/tensor.py

@@ -1,8 +1,13 @@
 import numpy as np
 
+def tensor(*args, **kwargs):
+  return Tensor(*args, **kwargs)
+
 class Tensor:
   # TODO Implement 'requires_grad' functionality.
   def __init__(self, value):
+    # NOTE We technically could support both numpy arrays and scalar values,
+    # but it is too much work.
     if not isinstance(value, np.ndarray):
       print(f"{type(value)} is not compatible with {np.ndarray}")
       exit(-1)
@@ -43,6 +48,17 @@ class Tensor:
     tensor._back = back
     return tensor
 
+  def div(self, other):
+    tensor = Tensor(self.value / other.value)
+    tensor._save(self, other)
+
+    def back(upstream):
+      a, b = tensor._parents
+      return 1 / np.dot(b.value, upstream), -a.value / np.dot(b.value ** 2, upstream)
+
+    tensor._back = back
+    return tensor
+
   def expt(self, exponent):
     tensor = Tensor(self.value ** exponent)
     tensor._save(self)
@@ -76,6 +92,18 @@ class Tensor:
     tensor._back = back
     return tensor
 
+  def tanh(self):
+    tensor = Tensor(np.tanh(self.value))
+    tensor._save(self)
+
+    def back(upstream):
+      # dtanh(x)/dx = 1 - tanh2(x)
+      a, = tensor._parents
+      return [1 - np.dot(np.tanh(a.value) ** 2, upstream)]
+
+    tensor._back = back
+    return tensor
+
   # TODO Compute gradients only for tensors that need it.
   def _backprop(self, upstream):
     # Backprop through the tensor iff it has any parents.